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Hyperspectral Image Classification Using Deep Learning Technique

  • Mayar A. ShafaeyEmail author
  • Mohammed A.-M. Salem
  • Maryam N. Al-Berry
  • Hala M. Ebied
  • Elsayed A. El-Dahshan
  • Mohammed F. Tolba
Conference paper
  • 180 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)

Abstract

Today, the classification process is demanded for modern city planning, agriculture and environmental monitoring, and many other applications. The optimum classification degree is still insufficient so far. The present classification methods for remote-sensing images are grouped according to the features they use into: manual feature-based methods, unsupervised feature learning methods, and supervised feature learning methods. In recent times, the supervised deep learning approaches are extensively introduced in various remote-sensing applications, such as object detection and land use scene classification. In this article, an experiment is conducted using one of the widespread deep learning models, Convolution Neural Networks (CNNs), specifically, AlexNet architecture on a standard sounded hyper spectral dataset, Pavia University (PaviaU). The model achieved an overall accuracy of 91% ± 0.01. A comparison with other different techniques is also introduced.

Keywords

Deep learning Convolution Neural Networks (CNNs) Remote-sensing Satellite images Hyperspectral images 

References

  1. 1.
    NASA: What is a satellite? NASA Knows! (Grades 5–8) series (2014)Google Scholar
  2. 2.
    Zhang, L., Xia, G., Wu, T., Lin, L., Tai, X.: Deep learning for remote sensing image understanding. J. Sens. 2016, 1–2 (2016)Google Scholar
  3. 3.
    Yaniv, O., Isaac, A., Vladimir, F., Daniel, G., Adrian, S.: Compressive sensing hyperspectral imaging by spectral multiplexing with liquid crystal. J. Imaging 5(1), 3 (2019)Google Scholar
  4. 4.
    Roy, B., Lawrence, T., Mahdi, N.: Spectral imaging using a commercial colour-filter array digital camera. In: The Fourteenth Triennial ICOM-CC Meeting, pp. 743–750 (2005)Google Scholar
  5. 5.
    Adam, C.W., Vincent, G.A., Everett, A.H.: Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens. 4(6), 1671–1692 (2012)CrossRefGoogle Scholar
  6. 6.
    Jose, A.J.B., Pablo, J.Z., Lola, S., Elias, F.: Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sens. 47(3), 722–738 (2009)CrossRefGoogle Scholar
  7. 7.
    Elhadi, A., Onisimo, M., Denis, R.: Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol. Manag. 18(3), 281–296 (2010)CrossRefGoogle Scholar
  8. 8.
    Shaheera, R., Nicolas, D.: A split-and-merge approach for hyperspectral band selection. IEEE Geosci. Remote Sens. Lett. 14(8), 1378–1382 (2017)CrossRefGoogle Scholar
  9. 9.
    https://rslab.ut.ac.ir/data Accessed 25 May 2019
  10. 10.
    Zhou, Z., Edoardo, P., Melba, M.C., James, C.T.: An active learning framework for hyperspectral image classification using hierarchical segmentation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 9(2), 640–654 (2016)CrossRefGoogle Scholar
  11. 11.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)CrossRefGoogle Scholar
  12. 12.
    Shalaby, M.M., Salem, M.A.-M., Khamis, A., Melgani, F.: Geometric model for vision-based door detection. In: 2014 9th International Conference on Computer Engineering & Systems (ICCES), Cairo, pp. 41–46 (2014).  https://doi.org/10.1109/icces.2014.7030925
  13. 13.
    Salem, M.A.-M., Appel, M., Winkler, F., Meffert, B.: FPGA-based smart camera for 3D wavelet-based image segmentation. In: 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC, pp. 1–8 (2008).  https://doi.org/10.1109/icdsc.2008.4635720
  14. 14.
    Salem, M.A.-M., Alaa, A., Alaa, S., Marwa, S.: Recent survey on medical image segmentation. In: Computer Vision: Concepts, Methodologies, Tools, and Applications, pp. 129–169. IGI Global (2018).  https://doi.org/10.4018/978-1-5225-5204-8.ch006
  15. 15.
    Jolliffe, I.: Principal Component Analysis. Springer, New York (2002)zbMATHGoogle Scholar
  16. 16.
    Zhao, B., Zhong, Y., Zhang, L.: A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery. ISPRS J. Photogramm. Remote Sens. 116, 73–85 (2016)CrossRefGoogle Scholar
  17. 17.
    Olshausen, B.A., Field, D.J.: Sparse coding with an over complete basis set: a strategy employed by V1? Vis. Res. 37(23), 3311–3325 (1997)CrossRefGoogle Scholar
  18. 18.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations, pp. 1–13 (2015)Google Scholar
  21. 21.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Salakhutdinov, R., Hinton, G.: An efficient learning procedure for deep Boltzmann machines. Neural Comput. 24(8), 1967–2006 (2012)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings Conference on Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  24. 24.
    Luus, F., Salmon, B., Van Den Bergh, F., Maharaj, B.: Multiview deep learning for land-use classification. IEEE Geosci. Remote Sens. Lett. 12(12), 2448–2452 (2015)CrossRefGoogle Scholar
  25. 25.
    Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land Use Classification in Remote Sensing Images by Convolutional Neural Networks. Cornell University, Ithaca (2015)Google Scholar
  26. 26.
    Zhong, Y., Fei, F., Zhang, L.: Large patch convolutional neural networks for the scene classification of high spatial resolution imagery. Appl. Remote Sens. 10(2), 025006 (2016)CrossRefGoogle Scholar
  27. 27.
    Marmanis, D., Datcu, M., Esch, T., Stilla, U.: Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geosci. Remote Sens. Lett. 13(1), 105–109 (2015)CrossRefGoogle Scholar
  28. 28.
    Shafaey, M.A., Salem, M.A.-M., Ebied, H.M., Al-Berry, M., Tolba, M.F.: Deep learning for satellite image classification. In: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, vol. 4, pp. 383–391 (2019)Google Scholar
  29. 29.
    Baofeng, G., Robert, I.D., Steve, R.G., James, D.B.: Improving hyperspectral band selection by constructing an estimated reference map. Appl. Remote Sens. 8, 083692 (2014)CrossRefGoogle Scholar
  30. 30.
    Hu, F., Xia, G., Wang, Z., Huang, X., Zhang, L., Sun, H.: Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2015–2030 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computers and Information SciencesAin Shams UniversityCairoEgypt
  2. 2.Faculty of Media Engineering and TechnologyGerman University in CairoCairoEgypt
  3. 3.Faculty of Science, Department of PhysicsAin Shams UniversityCairoEgypt
  4. 4.National Egyptian E-Learning University (EELU)GizaEgypt

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